In the second approach, we apply a semi-supervised learning approach called self-trained random survival forest (ST-RSF). In particular, we use the widely used self-training wrapper technique [1]}. Figure REF  shows the learning process in our self-training algorithm. This technique first builds an initial model using RSF over the labeled (in our case, observed and censored) data points and then iteratively augments the labeled set with the most confident predictions of survival time for the unlabeled dataset. In order to predict the survival time for each individual, we calculate the expected future lifetime (\(T_p\) ) which at a given time \(t_0\)  is the time remaining until the event, given that the event did not occur until \(t_0\)  [2]}:
\( T_p =\frac{1}{S(t_0)}\int _{t_0}^{\infty } S(t)dt\) 
